Improving the Critic Learning for Event-Based Nonlinear H∞ Control Design
Date of Original Version
In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H∞ state feedback control design. First of all, the H∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.
IEEE Transactions on Cybernetics
Wang, Ding, Haibo He, and Derong Liu. "Improving the Critic Learning for Event-Based Nonlinear H∞ Control Design." IEEE Transactions on Cybernetics 47, 10 (2017): 3417-3428. doi:10.1109/TCYB.2017.2653800.